Human Action Recognition in Still Image Using Weighted Bag-of-Features and Ensemble Decision Trees 


Vol. 38,  No. 1, pp. 1-9, Jan.  2013


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  Abstract

This paper propose a human action recognition method that uses bag-of-features (BoF) based on CS-LBP (center-symmetric local binary pattern) and a spatial pyramid in addition to the random forest classifier. To construct the BoF, an image divided into dense regular grids and extract from each patch. A code word which is a visual vocabulary, is formed by k-means clustering of a random subset of patches. For enhanced action discrimination, local BoF histogram from three subdivided levels of a spatial pyramid is estimated, and a weighted BoF histogram is generated by concatenating the local histograms. For action classification, a random forest, which is an ensemble of decision trees, is built to model the distribution of each action class. The random forest combined with the weighted BoF histogram is successfully applied to Standford Action 40 including various human action images, and its classification performance is better than that of other methods. Furthermore, the proposed method allows action recognition to be performed in near real-time.

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  Cite this article

[IEEE Style]

J. Hong, B. Ko, J. Nam, "Human Action Recognition in Still Image Using Weighted Bag-of-Features and Ensemble Decision Trees," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 1, pp. 1-9, 2013. DOI: .

[ACM Style]

June-hyeok Hong, Byoung-chul Ko, and Jae-yeal Nam. 2013. Human Action Recognition in Still Image Using Weighted Bag-of-Features and Ensemble Decision Trees. The Journal of Korean Institute of Communications and Information Sciences, 38, 1, (2013), 1-9. DOI: .

[KICS Style]

June-hyeok Hong, Byoung-chul Ko, Jae-yeal Nam, "Human Action Recognition in Still Image Using Weighted Bag-of-Features and Ensemble Decision Trees," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 1, pp. 1-9, 1. 2013.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880